چکیده (انگلیسی):

Due to growth of Cloud computing usage, the need to apply encrypted protocols to provide confidentiality
and integrity of data increases dramatically. Attacker can take advantage of these protocols to hide the
intrusion and evade detection. Many traditional attack detection techniques have been proposed to provide
security in the networks but none of them can be implemented properly in encrypted networks. This study
investigates a popular attack in Secure Shell (SSH), known as brute force attack and provides an efficient
method to detect this attack. Brute force attack is launched by implementing a client-server SSH model in a
private Cloud environment and the traffics regarding attack and normal are captured on the server. Then,
representative features of traffic are extracted and used by the Multi-Layer Perceptron model of Artificial
Neural Network to classify the attack and normal traffic. Results gained by this method show that the
proposed model is successfully capable to detect this attack with high accuracy and low false alarm.